AI-Integrated Aquatic Safety and Olympic Biomechanical Performance System: Smart Swimming Cap Inertial-Kinematics Drowning Prevention (< 3.0 s Alert Latency), Navier-Stokes Stroke Optimization, and Sobolev-HOCBF Safety Interlocks (AIを活用した水中安全・競泳パフォーマンス統合システム)

Author: Yoko Hasebe (長谷部 洋子) | Original Proposal Date: May 16, 2026 This monograph (Record #212) archives and mathematically formalizes the original May 16, 2026 bilingual project proposal by Yoko Hasebe (長谷部 洋子), titled "AI-Integrated Aquatic Safety & Performance System (AIを活用した水中安全・パフォーマンス統合システム)". The architecture establishes a dual-phase wearable and edge-AI system embedded within a smart swimming cap to address two critical challenges in aquatic environments: (1) Phase 1 — Drowning Prevention System (水難事故防止システム): Prevention of drowning fatalities in schools (~32,000 elementary, junior high, and high schools in Japan), municipal public pools, and coastal areas (where Japan records ~700-800 drowning deaths annually) via a smart swimming cap equipped with a positioning chip, 6-axis accelerometer/gyroscope, stroke counter, wireless telemetry relay, and immediate alert system. Trained on drowning behavioral signatures (panic movements, stroke irregularities, and vertical submersion), the system achieves sub-3.0-second alert latency (< 3.0 s) governed by a High-Order Control Barrier Function (Sobolev-HOCBF). (2) Phase 2 — Olympic Performance System (オリンピック競泳パフォーマンスシステム): Extension of the identical smart-cap sensor array to elite competitive swimming (targeting the 2028 Los Angeles Olympics as a showcase event), collecting seven biomechanical metrics (stroke count/frequency per lap, stroke efficiency ratio, turn execution time and push-off force estimation, head position/rotation angles, breathing timing, fatigue detection via movement degradation, and split-time correlation) and applying reinforcement learning to generate personalized coaching optimizations while protecting student and athlete biometric privacy via Goldilocks QFS-zkVM. License & Integrity Covenant: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026).